Resource Provisioning in SLA-Based Cluster Computing

Xiong, Ke; Suh, Sang C.

doi:10.1007/978-3-642-16505-4_1

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Multi-Layer Perceptron has been employed [2] [4] to analyze resource provisioning by profiling scientific applications (CPU-intensive applications) along with job history data in a heterogeneous computing environment. An approach to minimize the total cost of resources in a cluster computing environment [3] used by an application service provider has been presented which might pave way for appropriate provisioning of resources. Cost-aware and failure-aware provisioning policies [5] are proposed which could be employed on a virtual machine-based cluster.…”

Section: Related Workmentioning

confidence: 99%

Load Balancing for Effective Resource Provisioning in a Heterogeneous Cluster using Machine Learning

Velayutham¹,

Chandrasekaran²

2019

IJEAT

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Compute Clusters are typically installed to increase performance and/or accessibility. Appropriate Resource Provisioning is a key feature in clustered computing environments to avoid provisioning resources lower than the actual requirement and provisioning of resources in excess. In this paper, a load balancing scheme leading to effective provisioning of resources have been proposed. Job History of compute-intensive jobs have been collected by conducting experiments to observe basic parameters of a job in a heterogeneous computing cluster environment. A Machine Learning model using Multi-Layer Perceptron and Support Vector Machine for provisioning of resources has been presented. The prediction model uses the job history collected from the cluster environment to predict the resource that would be appropriate for provisioning in future. The accuracy of the model is computed and the results of experiments show that Multi-Layer Perceptron presents a better performance than Support Vector Machine

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Section: Related Workmentioning

confidence: 99%

Load Balancing for Effective Resource Provisioning in a Heterogeneous Cluster using Machine Learning

Velayutham¹,

Chandrasekaran²

2019

IJEAT

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“…Another economic consideration for Web scheduling is accurate accounting and charging for resources while providing the agreed-upon SLAs [29]. This may not be an issue when all the services are internal to the same company with a single economic interest (such as Facebook and Google), but it is a critical issue for shared server farms, such as Amazon's EC2 cloud [1].…”

Section: Accountingmentioning

confidence: 99%

Web-Scale Job Scheduling

Cirne

Frachtenberg

2013

Job Scheduling Strategies for Parallel Processing

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Abstract. Web datacenters and clusters can be larger than the world's largest supercomputers, and run workloads that are at least as heterogeneous and complex as their high-performance computing counterparts. And yet little is known about the unique job scheduling challenges of these environments. This article aims to ameliorate this situation. It discusses the challenges of running web infrastructure and describes several techniques to address them. It also presents some of the problems that remain open in the field.

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